An important factor that determines the stability and efficiency of a photovoltaic power station is cloud occlusion of sunlight. Unfortunately, the cloud dynamics in a local area within a short time horizon, for example about 20 minutes, cannot be accurately predicted using conventional computational techniques. Work has been done with camera-based systems which provide potential for fulfilling cloud dynamics estimation. These systems capture images of the sky continuously over periodic intervals, for example, every few seconds. Through analysis of the time series of images a reasonable estimate of cloud trajectories may be obtained. Predictions of when and how much sunlight will be occluded in the near future may be made through the analysis.
The camera system is calibrated and the captured images are transformed into the physical space or their Cartesian coordinates, referred to as the sky space. The clouds captured in the images are segmented and their motion is estimated to predict the cloud occlusion of the sun. For cloud segmentation, algorithms based support vector machine (SVM) and random forest have been proposed. To perform motion estimation, Kalman filtering, correlation and variational optical flow methods have been described in the literature. Techniques for long term predictions have been proposed, however, short term uncertainty is not addressed. A relatively short term (e.g. intra-hour) forecast confidence has been proposed correlating point trajectories with forecast error, with longer trajectory length corresponding to smaller forecast error. But using trajectory length as a criterion requires that the estimate be made only after the trajectory is completed. Thus, estimates at each image sample cannot be obtained.